add split_branch example

notebooks/split_branch.ipynb

@@ -0,0 +1,272 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install octave\n",
+    "!sudo apt-get -qq update\n",
+    "!sudo apt-get -qq install octave octave-signal liboctave-dev\n",
+    "\n",
+    "# install oct2py that compatible with colab\n",
+    "import os\n",
+    "\n",
+    "from pkg_resources import get_distribution\n",
+    "\n",
+    "os.system(\n",
+    "    f\"pip install -qq\"\n",
+    "    f\" ipykernel=={get_distribution('ipykernel').version}\"\n",
+    "    f\" ipython=={get_distribution('ipython').version}\"\n",
+    "    f\" tornado=={get_distribution('tornado').version}\"\n",
+    "    f\" oct2py\"\n",
+    ")\n",
+    "\n",
+    "# install packages\n",
+    "!pip install -qq matpower matpowercaseframes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Impedance Refactor for Parallel Lines\n",
+    "\n",
+    "## Objective\n",
+    "To model a single impedance $ Z $ as $ N $ parallel lines in a power system data file.\n",
+    "\n",
+    "## Formula\n",
+    "\n",
+    "### Impedance\n",
+    "\n",
+    "For $ N $ parallel lines with equal impedance:\n",
+    "\n",
+    "$$\n",
+    "\\frac{1}{Z_{\\text{total}}} = \\frac{1}{Z_1} + \\frac{1}{Z_2} + \\dots + \\frac{1}{Z_n} = \\frac{N}{Z_1} \\quad \\Rightarrow \\quad Z_{\\text{total}} = \\frac{Z_1}{N}\n",
+    "$$\n",
+    "\n",
+    "To refactor the impedance for each line, multiply the original impedance $ Z $ by $ N $:\n",
+    "\n",
+    "$$\n",
+    "Z_n = Z \\times N\n",
+    "$$\n",
+    "\n",
+    "Where $ Z = R + jX $ (resistance and reactance).\n",
+    "\n",
+    "### Susceptance\n",
+    "\n",
+    "For $ N $ parallel lines with equal susceptance:\n",
+    "\n",
+    "$$\n",
+    "B_{\\text{total}} = B_1 + B_2 + \\dots + B_n = N \\times B_1\n",
+    "$$\n",
+    "\n",
+    "To refactor the susceptance for each line, divide the original susceptance $ B $ by $ N $:\n",
+    "\n",
+    "$$\n",
+    "B_{\\text{new}} = \\frac{B}{N}\n",
+    "$$\n",
+    "\n",
+    "### Rate\n",
+    "\n",
+    "For $ N $ parallel lines with equal rate $ C $:\n",
+    "\n",
+    "$$\n",
+    "C_{\\text{total}} = C_{1} + C_{2} + \\dots + C_{n} = N \\times C_1\n",
+    "$$\n",
+    "\n",
+    "To refactor the rate for each line, divide the original rate $ C $ by $ N $:\n",
+    "\n",
+    "$$\n",
+    "C_n = \\frac{C}{N}\n",
+    "$$\n",
+    "\n",
+    "Where $ C $ long term, short term and emergency rating in MVA.\n",
+    "\n",
+    "## Example\n",
+    "Original impedance $ Z = 0.01 + j0.02 $ with line capacity $ C = 75 $ and total line charging susceptance $ B = 0.15$ , splitted into three parallel lines $ N = 3 $:\n",
+    "\n",
+    "$$\n",
+    "Z_{\\text{new}} = Z \\times N = (0.01 + j0.02) \\times 3 = 0.03 + j0.06\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "B_{\\text{new}} = B / N = 0.15 / 3 = 0.05\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "C_{\\text{new}} = C / N = 75 / 3 = 25\n",
+    "$$\n",
+    "\n",
+    "We then have N row in branch data, with each using $ Z_{\\text{new}} $, $ B_{\\text{new}} $, and $ C_{\\text{new}} $."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matpowercaseframes\n",
+    "import oct2py\n",
+    "\n",
+    "import matpower\n",
+    "\n",
+    "print(f\"matpowercaseframes version: {matpowercaseframes.__version__}\")\n",
+    "print(f\"oct2py version: {oct2py.__version__}\")\n",
+    "\n",
+    "print(f\"matpower version: {matpower.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "from matpowercaseframes import CaseFrames\n",
+    "from matpowercaseframes.constants import COLUMNS\n",
+    "\n",
+    "from matpower import path_matpower, start_instance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspect data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = start_instance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = os.path.join(path_matpower, \"data/case9.m\")\n",
+    "cf = CaseFrames(path, load_case_engine=m)\n",
+    "cf.infer_numpy()\n",
+    "cf.branch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run base"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mpc_org = cf.to_mpc()\n",
+    "sol = m.runpf(mpc_org, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame(sol[\"branch\"], columns=COLUMNS[\"branch\"][:17])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Edit branch to became N parallel lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 2\n",
+    "cf.branch = pd.concat([cf.branch] * N, ignore_index=True)\n",
+    "cf.branch.loc[:, [\"BR_R\", \"BR_X\"]] = cf.branch.loc[:, [\"BR_R\", \"BR_X\"]] * N\n",
+    "cf.branch.loc[:, [\"BR_B\", \"RATE_A\", \"RATE_B\", \"RATE_C\"]] = (\n",
+    "    cf.branch.loc[:, [\"BR_B\", \"RATE_A\", \"RATE_B\", \"RATE_C\"]] / N\n",
+    ")\n",
+    "cf.branch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mpc_new = cf.to_mpc()\n",
+    "sol = m.runpf(mpc_new, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame(sol[\"branch\"], columns=COLUMNS[\"branch\"][:17])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TODO: Show the new branch resiliency when there is branch contingency N - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}